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< Retour Visualisation techniques for social commentary AD 7 Hub: All Year: 2024-2026 Emanuelle Dufour, PDF A key component of the social dimensions of how adaptive silvicultural strategies are perceived by the general public will be presenting communities with different scenarios. To do so, visualisations of these different scenarios is critical to allow the public to get a sense of how, for example, different thinning regimes would look across the landscape. The advent of 3D data and computer visualisation techniques now allows novel, realistic (in a visual sense) and accurate representations of the forest landscapes managed under different scenarios. An RA with experience in visualisation, design and computer graphics will lead this project. They will bring their expertise to each Hub site and use available ALS data with the objective to allow simulations of scenarios proposed in Silva21. Outcome: A suite of visualisation scenarios that will drive the public consultation and social outreach with communities. Emanuelle Dufour, PDF, Université Laval Supervisor: Anne Bernard ​

PROJETS Adaptive silviculture for climate change (ASCC) trial AD 10 Hub: Petawawa research forest, ON Year: 2023-2026 Lisa Han See the project Learning from the past: key stand attributes linked with resilience AD 2 Hub: All Year: 2021-2024 Tommaso Trotto, PhD See the project Rainfall exclusion experiment: the effect of thinning AD 3c Hub: Black Brook, NB; Nova Scotia, NS Year: 2023-2025 Chloe Larstone Hunt, MSc See the project Climate change and adaptative silviculture: playing to collaborate with a serious game AD 5b Hub: All Year: 2023-2025 Sandrine Paquin, MSc See the project Silvicultural practices at the pace of global changes: a public policy challenge AD 8a Hub: All Year: 2022-2023 Anne Bernard, PDF See the project Assisted migration trials: early response AD 9b Hub: All Year: 2022-2025 Jacob Ravn, PhD See the project Impact of climate change on growth of commercial forest species in Nova Scotia AN 1c Hub: Nova Scotia Year: 2022 - 2024 Florence Leduc, M.Sc. See the project Tree ring characterization AN 2 Hub: All Year: 2023-2025 Emmannuelle Baby-Bouchard, RA See the project Stem vigour and growth of tolerant hardwoods AN 4 Hub: Haliburton, ON Year: 2022-2024 Guillaume Moreau, PDF See the project Thinning as a tool to increase resistance to stressors (AN6b) AN 6b Hub: Quesnel, BC Year: 2022-2024 See the project Viability of climate-informed, landscape-level strategies AN 8b Hub: All (West focus) Year: 2022-2025 Kirk Johnson, PhD See the project Optimization of the characterization of burning patterns OB 1b Hub: Lac-St-Jean, QC Year: 2021-2022 Gabrielle Thibault, MSc See the project Early alert system for forest management OB 3b Hub: Estrie, QC; Montmorency forest, QC; Lac-St-Jean, QC; Romeo Malette, ON Year: 2021-2025 Alexandre Morin-Bernard, PhD See the project LiDAR stem metrics for tree list models OB 4b Hub: Nova Scotia, NS; Estrie, QC Year: 2024-2025 See the project Revisiting existing trials AD 1a Hub: All Year: 2023-2025 Ethan Ramsfield See the project Silvicultural scenarios to promote resilient stand structures (AD3a) AD 3a Hub: Quesnel, BC Year: 2021-2022 Rover Liu, MSc See the project Salvage harvesting of dead trees AD 4 Hub: Quesnel, BC; Lac-St-Jean, QC Year: 2024-2025 See the project Culturally important species AD 6 Hub: Eastern Townships, QC Year: 2021-2022 Laurence Boudreault, PhD See the project White papers: final project outcomes AD 8b Hub: All Year: 2024-2026 Amy Wotherspoon, RA See the project Climatic drivers of tree growth AN 1a Hub: All Year: 2021 Catherine Chagnon, RA See the project Wood properties as proxies for past climate conditions AN 1d Hub: Ontario and Quebec Year: 2023-2025 Philippe Riel, MSc See the project Integration of climate drivers into growth modelling (AN3a) AN 3a Hub: Eastern Townships, QC Year: 2022-2025 Christina Howard, PhD See the project Targeted assisted migration AN 5 Hub: All Year: 2022-2025 João Paulo Czarnecki de Liz, PhD See the project Tree-level response to thinning AN 7 Hub: Montmorency research forest, QC; Lac-St-Jean, QC Year: 2021-2023 Marilou Yargeau, MSc See the project Flexibility in forest management to preserve caribou habitat AN 9 Hub: Newfoundland, NL Year: 2021-2022 Catherine Beaulieu, MSc See the project Advanced RS: free-to-grow to thinning stage OB 2 Hub: Romeo Malette, ON; Quesnel, BC Year: 2021-2025 Liam Irwin, PhD See the project From theory to action at the Montmorency Forest OB 3c Hub: Montmorency Forest Year: 2023-2024 Recruiting, RA See the project Revisiting existing trials II AD 1b Hub: All Year: 2024-2026 Meghan Clayton See the project Using state-of-the-art technology to achieve multiple forest management objectives (AD3b) AD 3b Hub: Quesnel, BC Year: 2023-2024 Mario Stolz, MSc See the project Deliberative-analytic framework to engage publics and stakeholders AD 5a Hub: Quesnel, BC Year: 2022-2025 Dane Pedersen, PhD See the project Visualisation techniques for social commentary AD 7 Hub: All Year: 2024-2026 Emanuelle Dufour, PDF See the project Assisted migration trials: implementation AD 9a Hub: All Year: 2023-20224 Recruiting, RA See the project Future climate envelopes AN 1b Hub: All Year: 2021-2022 Amy Wotherspoon, PDF See the project Impact of acute climatic events on tree growth AN 1e Hub: All Year: 2023-2026 Sébastien Dumont, PhD See the project Integration of climate drivers into growth modelling (AN3b) AN 3b Hub: Black Brook, NB; Acadia, NB; Nova Scotia Year: 2021-2023 Jamie Ring, MSc See the project Thinning as a tool to increase resistance to stressors AN 6a Hub: Quesnel, BC Year: 2022-2024 Sergio Alonso Sanchez, MSc See the project Forest management plans for resilient landscapes AN 8a Hub: All (East focus) Year: 2023-2026 Helin Dura, PhD See the project Regeneration after catastophic disturbance OB 1a Hub: Quesnel, BC; Malcolm Knapp, BC Year: 2021-2024 Sarah Smith-Tripp, PhD See the project Seasonal mosaics of forest cover OB 3a Hub: All Year: 2021-2023 Micheal Burnett, RA See the project LiDAR EFI for growth projections: new approaches OB 4a Hub: Romeo Malette, ON Year: 2022-2024 José Riofrío , PDF See the project

< Retour Characterization of regeneration failure in the Canadian boreal forest using satellite imagery and airborne laser scanning data OB 7 Hub: RM, LSJ, MM Year: 2024 - 2028 Shaya (Fatemah) Gholami, PhD Boreal forests constitute the largest carbon reservoir globally, providing a wide array of ecosystem services and serving as an essential timber source for societal needs. Natural and anthropogenic disturbances can profoundly alter the structure and composition of these ecosystems. While the complex interactions between climate change, the frequency, severity, and extent of these disturbances, and the forest regeneration trajectories have received considerable attention in recent decades, their exact consequences remain poorly understood. The objective of this project is to characterize and assess the magnitude of regeneration failures following clearcutting and forest fires at different locations in the Eastern boreal forest of Canada. By combining satellite images from various sensors with airborne laser scanning (ALS) data, this project will bring new insights into post-disturbance regeneration dynamics and identify factors responsible for the variability in the success of regeneration establishment. The results will help quantify the impact of regeneration failures on timber yield and the provision of ecosystem services, and allow for the spatially explicit identification of areas that should be prioritized for targeted silvicultural actions. Shaya (Fatemah) Gholami, PhD student Université Laval Partners: Canadian Space Agency,, Ministère des forêts, de la faune et des parcs (Québec) Supervisor: Alexandre Morin Bernard ​

DONNÉES CLIMATIQUES Rapport Climatique Silva21 Un rapport complèt résumant les projections climatiques futures pour chacun des 12 hubs dans le projet du Silva21 Wotherspoon, A.R., Burnett, M., Achim, A., Coops, N.C. 2022. Climate Scenarios for Canadian Forests. Silva21, Vancouver, B.C. DOWNLOAD Les données climatiques ont été collectées et analysées pour chacun des hubs. La majorité des données climatiques ont été consultées via ClimateNA . Plusieurs rapports climatiques ont été produits et sont disponibles sur le portail Globus . Si vous souhaitez plus d'informations, veuillez consulter Amy Wotherspoon . ​ ​ Application de sélection de modèles de circulation globale SilvR21 Cet outil fournit des visualisations et de la documentation de l'ensemble de modèles climatiques mondiaux présenté dans la version 7 de ClimateNA. L'ensemble vient de la nouvelle génération de simulations de modèles climatiques mondiaux (General Circulation Models), le sixième projet d'intercomparaison de modèles couplés (CMIP6). Utilisez cet outil pour en savoir plus sur les simulations de modèles au sein de chaque site hub Silva21 et choisissez un petit ensemble adapté à votre recherche. SilvR21 Application Accéder au GitHub de Silva21 Accéder la vignette SilvR21 Pour une description plus détaillée de l'utilisation de ce package R, consultez notre document de bienvenue Package R - SilvR21 Pour simplifier les méthodes d'accès et de traitement des données climatiques de ClimateNA, le package R SilvR21 a été crée par Michael Burnett. Il fournit des fonctions pour: Préparer des modèles numériques d'élévation (DEM) sous forme de fichiers TIFF et les transfère au format CSV (une exigence nécessaire pour ClimateNA) D'accéder aux données climatiques projetées historiques et futures Traiter les fichiers d'une manière similaire à la façon dont il a été utilisé jusqu'à présent dans Silva21. Les moyennes/sommes annuelles et saisonnières peuvent être calculées à partir de données mensuelles avec des générateurs d'ensemble GCM personnalisés. ​ Pour importer SilvR21 sur votre ordinateur: Assurez-vous que le package devtools est installé. Ensuite, utilisez devtools::install_github("Silva21-irss/silvR21",build_vignettes = TRUE) pour l'installer sur votre ordinateur. Suivez la vignette R pour le package silvR21. Après avoir téléchargé le package, vous pouvez le rechercher avec browserVignettes('silvR21') .

DONNÉES Portail de données (Globus) Données climatiques Données par hub

< Retour Optimization of the characterization of burning patterns OB 1b Hub: Lac-St-Jean, QC Year: 2021-2022 Gabrielle Thibault, MSc Despite being less frequent in the East, fires remain the main cause of natural disturbances in the boreal forests of Quebec (QC) and Ontario (ON) and regeneration success is highly variable in forests dominated by black spruce. An MSc student will collaborate with the PhD student (OB.1a) and adapt the workflow to the LSJ and RM boreal Hubs. Using metrics of natural stand establishment that characterize failures or successes, the objective will be to improve our understanding the ecological factors that lead to black spruce regeneration after fire disturbance. Outcome (OB.1b): Identification of key stand- and landscape-level characteristics that favour regeneration success and improve forest resilience. Gabrielle Thibault, MSc at Université Laval Main Partner: Ministère des Forêts, de la Faune et des Parcs (Québec) Professor Alexis Achim ​

L'ÉQUIPE La direction scientifique du programme est assurée par Alexis Achim et Nicholas Coops. Douze chercheurs principaux et environ 50 collaborateurs de tout le pays contribueront à cet effort de recherche sans précédent dans le domaine de la sylviculture. Le projet réunit un consortium de cinq universités, cinq entreprises forestières privées, une communauté des Premières Nations, cinq agences gouvernementales provinciales, ainsi que le Centre Canadien sur la Fibre de Bois (CCFB) et FPInnovations en tant qu'organismes de recherche nationaux. Structure de gouvernance Direction Scientifique Codemandeurs Contacts clés PHQ Partenaires Direction scientifique Codemandeurs Alexis Achim, UL alexis.achim@sbf.ulaval.ca Nicholas Coops, UBC nicholas.coops@UBC.CA Direction Scienifique Codemandeurs Brad Pinno, UoA bpinno@UALBERTA.CA Charles Nock, UoA nock@UALBERTA.CA Evelyne Thiffault, UL Evelyne.Thiffault@SBF.ULAVAL.CA Loïc D’Orangeville, UNB loic.dorangeville@UNB.CA Shannon Hagerman, UBC shannon.hagerman@UBC.CA Bianca Eskelson, UBC bianca.eskelson@UBC.CA Dominik Roeser, UBC dominik.roeser@UBC.CA John Caspersen, UoT john.caspersen@UTORONTO.CA Maude Flamand-Hubert, UL maude.flamand-hubert@SBF.ULAVAL.CA Contact clé pour les organisations partenaires Vincent Roy, CWFC Joanne White, CFS John MacLellan, Kruger Faron Knott, Kruger Jean Girard, MFFP-QC Jennifer Dacosta, NDMNRF Chris McDonell, GreenFirst Adam Gorgolewski, Haliburton Forest Jodi Axelson, BC MFLNRO Mathieu Blouin, FPInnovations Éric Lapointe, Domtar David Bernard, Grand-Conseil de la Nation Waban-Aki Kevin Jewett, NRR-NS Bruce Stewart, NRR-NS Chris Hennigar, DNRED NB Shane Furze, JDI Personnel hautement qualifié Sandrine Paquin, UL (projet complété) Laurence Boudreault, UL Anne Bernard, UL (projet complété) Catherine Chagnon, UL Amy Wotherspoon, UBC Christina Howard, UBC Jamie Ring, UBC Lisa Han, UofT José Riofrío, UBC Lukas Olson, UBC Mario Stolz, UBC Spencer Shields, UBC Guillaume Moreau, UofT (projet complété) João Paulo Czarnecki de Liz, UL Marilou Yargeau, UL ( projet complété) Catherine Beaulieu, UL Sarah Smith-Tripp, UBC Gabrielle Thibault, UL (projet complété) Chris Mulverhill, UBC Florence Leduc, UL Kirk Johnson, UBC Madison Brown, UBC Emmannuelle Baby-Bouchard, UL Ethan Ramsfield, UA Taylor Bottoms-Cau, UNB Alexandre Morin-Bernard, UL (projet complété) Sergio Alonso Sanchez, UBC Liam Irwin, UBC Tommaso Trotto, UBC Rover Liu, UBC Dane Pedersen, UBC Jacob Ravn, UNB Chloe Larstone Hunt, UNB Philippe Riel, UL Sébastien Dumont, UL Helin Dura, UL Michael Burnett (projet complété) Meghan Clayton, UAlberta Contacts clé PHQ Nos partenaires Partenaires

Reconnaissances territoriale Silva21 reconnaît les recherches en cours menées sur les territoires traditionnels, ancestraux et non cédés des communautés autochtones qui ont été leur foyer pendant d'innombrables générations. Nous reconnaissons les diverses cultures, langues et histoires autochtones qui ont façonné et continuent d'enrichir ces terres. Nous rendons hommage aux aînés, passés et présents, et exprimons notre gratitude aux communautés autochtones qui ont préservé les forêts et les terres sur lesquelles nous vivons, travaillons et jouons. Nous reconnaissons que les effets de la colonisation, passés et actuels, ont eu de profondes répercussions sur les peuples autochtones et leur relation avec la terre. Nous nous engageons à apprendre des communautés des Premières Nations et à travailler en partenariat avec elles pour promouvoir les principes de réconciliation, de respect et de collaboration dans le domaine de la foresterie. Alors nous invitons tous la communauté de Silva21 à réfléchir au rôle vital que jouent les connaissances, les perspectives et les voix autochtones dans l'élaboration de notre compréhension de l'écosystème forestier et de la gestion durable des forêts. Joignez-vous à nous pour vous engager dans des efforts continus visant à établir des relations significatives avec les communautés autochtones, à reconnaître leurs droits et leur souveraineté et à intégrer les connaissances et les perspectives autochtones dans nos recherches et nos pratiques dans un avenir de réconciliation, d'équité et de durabilité. Information par institutions University of British Columbia UBC Vancouver is located on the traditional, ancestral, and unceded territory of the Musqueam people. The land it is situated on has always been a place of learning for the Musqueam, who for millennia have passed on their culture, history, and traditions from one generation to the next on this site. University of Toronto The University of Toronto is located on the traditional territory of the Wendat, the Anishnaabeg, Haudenosaunee, Métis, and the Mississaugas of the New Credit First Nation. University of Alberta The University of Alberta, its buildings, labs, and research stations are primarily located on the traditional territory of Cree, Blackfoot, Métis, Nakota Sioux, Iroquois, Dene, and Ojibway/Saulteaux/Anishinaabe nations; lands that are now known as part of Treaties 6, 7, and 8 and homeland of the Métis. The University of Alberta respects the sovereignty, lands, histories, languages, knowledge systems, and cultures of First Nations, Métis and Inuit nations. Université Laval Dans un esprit d’amitié et de solidarité, l’Université Laval rend hommage aux Premiers Peuples de ces lieux. Étant à la croisée du Niowentsïo du peuple Huron-Wendat, du Ndakina du peuple Wabanaki, du Nitassinan du peuple Innu, du Nitaskinan du peuple Atikmekw et du Wolastokuk Malécite, nous honorons nos relations les uns avec les autres. University of New Brunswick UNB stands on the unsurrendered and unceded traditional Wolastoqey land. The lands of Wabanaki people are recognized in a series of Peace and Friendship Treaties to establish an ongoing relationship of peace, friendship and mutual respect between equal nations. En savoir plus sur la terre où vous vivez, travaillez et jouez Utilisez la carte interactive à www.native-land.ca pour en savoir plus sur les territoires, les langues et les traités des Premières Nations qui existent là où vous vivez, travaillez et jouez et partout dans le monde.

Acerca de RECRUTEMENT Poste d’étudiant diplômé (PhD) en sylviculture adaptative: migration assistée des arbres forestiers (projet TransX) Déscription du projet: La plupart des arbres au Canada sont menacés par le réchauffement climatique, ce qui nécessite l’élaboration de stratégies pour accroître la capacité d’adaptation des forêts. Ici, nous proposons de développer de nouvelles connaissances opérationnelles qui peuvent éclairer la migration assistée par la forêt, une composante clé des interventions forestières intelligentes face au climat. Cette nouvelle recherche s’appuiera sur l’essai TransX établi dans le cadre de l’initiative de recherche Silva21, qui vise à tester empiriquement la migration assistée de plusieurs espèces clés de conifères et de feuillus, afin d’améliorer la phase d’établissement du peuplement et d’assurer le maintien de peuplements forestiers sains et dynamiques sous l’effet du changement climatique. Le doctorant surveillera la réponse précoce des populations d’arbres forestiers adaptés à la chaleur plantées dans des sites plus froids du nord des États-Unis et de l’est du Canada et élaborera de nouvelles lignes directrices pour aider à faire passer les stratégies de migration assistée à des opérations réussies. ​ Profil du candidat : Maîtrise en foresterie, en environnement, en sciences biologiques ou dans un autre domaine pertinent. Une forte motivation et une très bonne autonomie sont souhaitées. Tous les candidats qualifiés seront considérés pour un emploi sans égard à la race, la couleur, la religion, le sexe, l’origine nationale ou le handicap. ​ Financial support : G uaranteed minimum annual income of $28,000 (CDN) for three years. Beginning of the PhD: May 2024. Contact : Send a cover letter, a transcript and a complete CV by email to Prof. Loïc D’Orangeville, Faculty of Forestry and Environmental Management, University of New Brunswick, at loic.dorangeville@unb.ca . The position is open until filled. ​ Voir l'annonce ici.

< Retour Climate-sensitive growth modelling in Ontario OB 7 Hub: All Year: 2022-2024 José Riofrio, PDF Throughout the Hub sites, significant field inventory data is already available, which cover many growth characteristics, disturbance regimes and stand structures that could be considered for future silvicultural scenarios. The data, however, has been acquired over long periods using different methods across sites. Big data and machine learning approaches are well designed to be able to extract underlying trends from large datasets, where more traditional approaches such as regression may fail. In this project, we will invest in such approaches with the objective to link climatic data with the long-term forest mortality and growth data. To achieve this, Lara Climaco de Melo (PDF) will compile existing plot datasets across Hub sites and use machine learning techniques to predict mortality and growth from past climate. In conjunction with existing efforts, we will develop an open-source tool to facilitate exchange of inventory data between Hub sites, provinces, and companies. Outcome: A workflow to identify key climate variables that affect tree growth and mortality. José Riofrio, PDF at University of British Columbia Main Partner: Canadian Wood Fibre Centre Professor: Bianca Eskelson Riofrio, J., White, J.C., Tompalski, P., Coops, N., Wulder, M.A (2023) Modelling height growth of temperate mixedwood forests using an age-independent approach and multi-temporal airborne laser scanning data. Forest Ecology and Management 543;121137. https://doi.org/10.1016/j.foreco.2023.121137 Riofrio, J., White, J.C., Tompalski, P., Coops, N., Wulder, M.A (2022) Harmonizing multi-temporal airborne laser scanning point clouds to derive periodic annual height increments in temperate mixedwood forests. Canadian Journal of Forest Research, 52(10): 1334-1352. https://doi.org/10.1139/cjfr-2022-0055

Estrie Research Forest Estrie is a 1611 square kilometres temperate hardwood forest hub site located in Quebec, south of Montreal and near the United States boarder. To gain access to more raw data, please contact Eric Lapointe, Nicolas Maegher, and Felix Brochu-Marier from Domtar. [Click on any image to magnify] Site Details Climate LiDAR Products Vector Products Forest Monitoring Anchor 1 Site Details Best Available Pixel (2020) Estrie Hub Site Boundary Estrie Digital Elevation Model (DEM) at 30 m resolution Best Available Pixel (BAP) composites use Landsat scenes to develop cloud-free, surface reflectance pixel-based image composites capable of large-area production. When incorporated in a time series, they generate land cover, land cover change, and forest structural attributes information products in a dynamic, transparent, systematic, repeatable, and spatially exhaustive manner. This figure displays the 2020 BAP composite within the Haliburton hub site in Ontario. The acquisition of all pixels for this BAP composite were within 30 days of the first of August, 2020. Estrie Digital Elevation Model (DEM) at 250 m resolution Anchor 2 Climate Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) Climate data for historical (1981-2010) and future (2050 and 2090) projections Anchor 3 LiDAR Derived Products Digital Elevation Model (1m) Description - An interpolation of last returns classified as 'ground' points using TIN. Pixel Values - Elevation at a 1 metre resolution. Forestry Application - The Digital Elevation Model is important for topographical information, including slope, aspect, and radiation Slope (1m) Description - Steepness or the degree of incline of a surface based on the DEM model Pixel Values - Raster containing numeric values representing degrees of incline Forestry Application - Slope influences tree stability, harvesting solutions, productivity of harvesting and collection means, architecture of the road networks, road characteristics, and solutions related to the reclamation of degraded forested land. Aspect (1m) Description - Orientation of slope, measured clockwise in radians based on the DEM model Pixel Values - Raster containing a numeric value representing the radians of orientation Forestry Application - Land oriented in northerly are typically wetter and cooler compared to land oriented in southerly Radiation (1m) Description - A technique to visualize a shaded relief, illuminating it with a hypothetical light Pixel Values - Raster containing a numeric value representing the solar-radiation aspect index. Values range from 0 (land oriented in a northern direction resulting in less solar radiation exposure) and 1 (land oriented with southern slopes) Forestry Application - Land oriented in northerly (values closer to zero) are typically wetter and cooler compared to land oriented in southerly (values closer to one) 20th Height Percentile (20m) Description - Height at which 20% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 20% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy 95th Height Percentile (20m) Description - Height at which 95% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 95% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Canopy Height Model (1m) Description - Based on an interpolation of the height of the top of trees (using the pitfree algorithm) Pixel Values - Raster containing a numeric value for the distance between the ground and the top of trees Forestry Application - Helpful for determining the distribution of canopy coverage Mean Height (20m) Description - Mean height of first returns above 2m from "ground" (last return data) Pixel Values - Mean height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the mean height of all objects (trees) that are at least 2m tall Mean Standard Deviation (20m) Description - Standard Deviation of height distributions above 2m Pixel Values - Standard deviation height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the standard deviation for the height of all objects (trees) that are at least 2m tall Entropy (20m) Description - Shannon entropy quantifies the diversity and evenness of an elevation distribution of LiDAR points from 2m above the ground Pixel Values - Entropy results range from 0 to 1. Random data has a Shannon entropy value of 1 Forestry Application - Useful for describing and quantifying species diversity in biological systems. Canopy Cover > 2m (20m) Description - Canopy cover at a height greater than 2 metres Pixel Values - Ratio from the sum of first returns > 2 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 2 metres LAIe (20m) Description - A measurement of the gap fraction through the probability of beam penetration of sunlight through the vegetation. Pixel Values - Ratio of one-sided green leaf area per unit ground surface area Forestry Application - Important growth index for the status of crop populations Skewness (20m) Description - A measure of the distribution's symmetry from 2m above the ground Pixel Values - A normal distribution would produce skewness results of zero. Negative values indicate that data is skewed to the left, and positive values indicate that data is skewed to the right. Forestry Application - Skewness is often used with kurtosis to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Kurtosis (20m) Description - The size of the tails of a distribution (likelihood that the distribution will produce outliers) from 2m above the ground Pixel Values - A normal distribution would produce kurtosis results of 3. Distributions with kurtosis less than 3 are platykurtic (fewer and less extreme outliers) and distributions with kurtosis greater than 3 are laptokurtic (produce more outliers) Forestry Application - Kurtosis is often used with skewness to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 5m (20m) Description - Canopy cover at a height greater than 5 metres Pixel Values - Ratio from the sum of first returns > 5 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 5 metres Canopy Cover > 15m (20m) Description - Canopy cover at a height greater than 15 metres Pixel Values - Ratio from the sum of first returns > 15 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 15 metres Rumple (20m) Description - Crown Surface Roughness from 2m above the ground Pixel Values - A ratio of canopy outer surface area to ground surface area Forestry Application - Higher rumple values indicate more vertical and horizontal heterogeneity Anchor 4 Vector Products

Quesnel Research Forest Quesnel is a 20,454 square kilometres dry inland forest hub site located in the middle of British Columbia, south of Prince George. To gain access to more raw data, please contact Jodi Axelson with the Government of British Columbia and Erin Robinson with the City of Quesnel. [Click on any image to magnify] Site Details Climate LiDAR Products Vector Products Forest Monitoring Anchor 1 Site Details Best Available Pixel (2020) Quesnel Hub Site Boundary Quesnel Digital Elevation Model (DEM) at 30 m resolution Best Available Pixel (BAP) composites use Landsat scenes to develop cloud-free, surface reflectance pixel-based image composites capable of large-area production. When incorporated in a time series, they generate land cover, land cover change, and forest structural attributes information products in a dynamic, transparent, systematic, repeatable, and spatially exhaustive manner. This figure displays the 2020 BAP composite within the Haliburton hub site in Ontario. The acquisition of all pixels for this BAP composite were within 30 days of the first of August, 2020. Quesnel Digital Elevation Model (DEM) at 250 m resolution Anchor 2 Climate Projections for change in minimum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in maximum temperature for the years 2050 and 2090 relative to the reference period (1981-2010) Projections for change in seasonal precipitation for the years 2050 and 2090 relative to the reference period (1981-2010) Climate data for historical (1981-2010) and future (2050 and 2090) projections Anchor 3 LiDAR Derived Products Digital Elevation Model (1m) Description - An interpolation of last returns classified as 'ground' points using TIN. Pixel Values - Elevation at a 1 metre resolution. Forestry Application - The Digital Elevation Model is important for topographical information, including slope, aspect, and radiation Slope (1m) Description - Steepness or the degree of incline of a surface based on the DEM model Pixel Values - Raster containing numeric values representing degrees of incline Forestry Application - Slope influences tree stability, harvesting solutions, productivity of harvesting and collection means, architecture of the road networks, road characteristics, and solutions related to the reclamation of degraded forested land. Aspect (1m) Description - Orientation of slope, measured clockwise in radians based on the DEM model Pixel Values - Raster containing a numeric value representing the radians of orientation Forestry Application - Land oriented in northerly are typically wetter and cooler compared to land oriented in southerly Radiation (1m) Description - A technique to visualize a shaded relief, illuminating it with a hypothetical light Pixel Values - Raster containing a numeric value representing the solar-radiation aspect index. Values range from 0 (land oriented in a northern direction resulting in less solar radiation exposure) and 1 (land oriented with southern slopes) Forestry Application - Land oriented in northerly (values closer to zero) are typically wetter and cooler compared to land oriented in southerly (values closer to one) 20th Height Percentile (20m) Description - Height at which 20% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 20% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy 95th Height Percentile (20m) Description - Height at which 95% of LiDAR returns fall below from 2m above the ground Pixel Values - Height measurement in metres describing the height at which 95% of LiDAR returns fall below Forestry Application - Assists in determining the height and distribution of the lower section of the canopy Canopy Height Model (1m) Description - Based on an interpolation of the height of the top of trees (using the pitfree algorithm) Pixel Values - Raster containing a numeric value for the distance between the ground and the top of trees Forestry Application - Helpful for determining the distribution of canopy coverage Mean Height (20m) Description - Mean height of first returns above 2m from "ground" (last return data) Pixel Values - Mean height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the mean height of all objects (trees) that are at least 2m tall Mean Standard Deviation (20m) Description - Standard Deviation of height distributions above 2m Pixel Values - Standard deviation height of all point cloud returns greater than 2m above last returns Forestry Application - Determines the standard deviation for the height of all objects (trees) that are at least 2m tall Entropy (20m) Description - Shannon entropy quantifies the diversity and evenness of an elevation distribution of LiDAR points from 2m above the ground Pixel Values - Entropy results range from 0 to 1. Random data has a Shannon entropy value of 1 Forestry Application - Useful for describing and quantifying species diversity in biological systems. Skewness (20m) Description - A measure of the distribution's symmetry from 2m above the ground Pixel Values - A normal distribution would produce skewness results of zero. Negative values indicate that data is skewed to the left, and positive values indicate that data is skewed to the right. Forestry Application - Skewness is often used with kurtosis to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Kurtosis (20m) Description - The size of the tails of a distribution (likelihood that the distribution will produce outliers) from 2m above the ground Pixel Values - A normal distribution would produce kurtosis results of 3. Distributions with kurtosis less than 3 are platykurtic (fewer and less extreme outliers) and distributions with kurtosis greater than 3 are laptokurtic (produce more outliers) Forestry Application - Kurtosis is often used with skewness to separate ground points and object points from a LiDAR point cloud. It has a variety of applications, including optimizing the DEM, segmentation and classification, and road extraction. Canopy Cover > 2m (20m) Description - Canopy cover at a height greater than 2 metres Pixel Values - Ratio from the sum of first returns > 2 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 2 metres Canopy Cover > 5m (20m) Description - Canopy cover at a height greater than 5 metres Pixel Values - Ratio from the sum of first returns > 5 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 5 metres Canopy Cover > 15m (20m) Description - Canopy cover at a height greater than 15 metres Pixel Values - Ratio from the sum of first returns > 15 metres divided by the total first returns Forestry Application - Important for determining the area occupied by the vertical projection of tree crowns greater than 15 metres Rumple = LAIe (20m) Description - A measurement of the gap fraction through the probability of beam penetration of sunlight through the vegetation. Pixel Values - Ratio of one-sided green leaf area per unit ground surface area Forestry Application - Important growth index for the status of crop populations Rumple (20m) Description - Crown Surface Roughness from 2m above the ground Pixel Values - A ratio of canopy outer surface area to ground surface area Forestry Application - Higher rumple values indicate more vertical and horizontal heterogeneity Anchor 4 Vector Products